Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
360-04-005 ISMRM Abstract

Physics-reinforced Implicit Neural Representation for Scan-specific Multiparametric qMRI Reconstruction

Primary: Acquisition & Reconstruction - Quantitative Imaging: relaxometry, multi-parametric, MR Fingerprinting, and synthetic MRI
Secondary: Contrast Mechanisms - Magnetization Transfer
360-04-005 · Quantitative Mapping: Acquisition and Reconstruction · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row A
Keywords: Implicit neural representation Self-supervised learning Quantitative Magnetization Transfer Model reinforcement Multiparametric quantitative MRI
Accepted
Ruimin Feng1,2, Albert Jang1,2, Xingxin He1,2, Fang Liu 1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
2Harvard Medical School, Boston, United States of America
Presenting Author: Fang Liu

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Liu F, Kijowski R, El Fakhri G, Feng L. Magnetic resonance parameter mapping using model-guided self-supervised deep learning. Magn Reson Med. 2021;85(6):3211-3226. doi:10.1002/mrm.28659 [doi]
2. Jun Y, Cho J, Wang X, et al. SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS. Magn Reson Med. 2023;90(5):2019-2032. doi:10.1002/mrm.29786 [doi]
3. Jun Y, Arefeen Y, Cho J, et al. Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS. Magn Reson Med. 2024;91(6):2459-2482. doi:10.1002/mrm.30018 [doi]
4. Heydari A, Ahmadi A, Kim TH, Bilgic B. Joint MAPLE: Accelerated joint T1 and T2* mapping with scan‐specific self‐supervised networks. Magn Reson Med. 2024;91(6):2294-2309. doi:10.1002/mrm.29989 [doi]
5. Bian W, Jang A, Liu F. Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping. Magn Reson Med. 2024;92(1):98-111. doi:10.1002/mrm.30045 [doi]
6. Lao G, Feng R, Qi H, et al. Coordinate-based neural representation enabling zero-shot learning for fast 3D multiparametric quantitative MRI. Med Image Anal. 2025;102:103530. doi:10.1016/j.media.2025.103530 [doi]
7. Zhao R, Peng X, Kelkar VA, Anastasio MA, Lam F. High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. IEEE Trans Biomed Eng. 2024;71(6):1969-1979. doi:10.1109/TBME.2024.3358223 [doi]
8. Mildenhall B, Srinivasan PP, Tancik M, Barron JT, Ramamoorthi R, Ng R. NeRF: representing scenes as neural radiance fields for view synthesis. Commun ACM. 2021;65(1):99-106. doi:10.1145/3503250 [doi]
9. Hu EJ, Shen Y, Wallis P, et al. LoRA: Low-Rank Adaptation of Large Language Models. October 2021. doi:10.48550/arXiv.2106.09685 [doi]
10. Jang A, Han PK, Ma C, et al. B1 inhomogeneity-corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects. Magn Reson Med. 2023;90(5):1859-1873. doi:10.1002/mrm.29778 [doi]
11. Zhang T, Pauly JM, Levesque IR. Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med. 2015;73(2):655-661. doi:10.1002/mrm.25161 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/360-04-005